What secondary NPI addresses add to an organization map

Our data

We computed this report from 27,965 NPI records in 13 existing legal-organization profiles. The downloadable dataset contains 13 rows at the legal organization level. The cohort consists of these selected legal-name profiles; each retains its own source boundaries.

Download the computed CSV · JSON dataset · Input checksums and provenance · Reproduction method

Novant accounts for most of the incremental coverage

Novant Health Medical Group LLC has 389 primary keys and 88 secondary keys in the retained extract. Of those secondary keys, 49 are absent from its primary set. Combining the sets produces 438 keys, an increase of 12.60% over the primary-only count. Adding all 88 to the primary count would double-count the 39 keys already present there.

That overlap is the reason for using sets. A secondary address can repeat on several registrations or already appear as another registration's primary address. Counting rows in a secondary-location field measures field population. Set subtraction measures the additional address keys it contributes to the organization's map.

A large registration file can produce a small increment

Walgreen Co contributes 11,140 NPI records and 10,381 primary keys to this cohort. Its secondary fields contain 14 distinct keys, of which 12 add to the primary set. The combined result is 10,393 keys, a 0.12% increase. Here, registration volume and the secondary-address increment differ sharply.

Publix Super Markets Inc. adds two keys to 1,161 primary keys. Total Renal Care Inc. adds one to 955. These small increments may still matter for a particular service territory; assess their commercial value with current local evidence. Follow the source records and verify the address before adding it to an active operating-location list.

Why a secondary key can add zero new coverage

The New York City Health and Hospitals Corporation profile has one secondary key and 36 primary keys. Its union remains 36 because the secondary key already exists in the primary set. Keeping the secondary relationship can still be useful: it records how one registration relates to an address. Its contribution is a retained relationship within the existing distinct-key count.

Eight other profiles have no secondary keys in the retained field. Together with the overlapping New York City case, that produces nine profiles with zero incremental coverage. Those two situations should remain distinguishable in a data-quality report. One says the source field is empty; the other says it supplies a relationship already represented elsewhere.

Store the relationship before making a facility claim

A location table can retain the normalized key, the original address string and its source. A separate relationship table can connect each NPI to that key with a primary or secondary role. This design preserves the detail needed to explain why an address entered the dataset without assuming that every relationship is a new facility.

If a downstream workflow requires a count of operating clinics, pharmacies or offices, it needs additional evidence. A current official location directory can help establish whether an address is in use. Distinguishing departments, suites and billing registrations requires record-level evidence. Keep that operational classification alongside the source address relationship.

A controlled review of additional keys

Start with the S-minus-P records, because those are the addresses that change a primary-only map. Check whether a difference comes from a different street location or from formatting. Preserve the original source strings while recording the reviewer’s decision. Record any matching adjustment separately and keep the underlying extract intact.

Next inspect the overlap set, S intersect P. It can reveal registrations that share an address relationship even though they have different primary locations. That describes the source's structure and provides candidates for a separate purchasing-authority review. Keep an ownership or account-hierarchy decision separate from the address evidence.

Interpret the cohort total carefully

The combined count of 24,198 sums each legal-name profile's union. The unit remains an address key within each of the 13 organizations. The same physical address can occur under more than one legal name and count more than once in this total. That is intentional because the unit is a legal-name and address-key pair.

A national market-size estimate would require a defined population and consistent collection across that population. These existing profiles were selected for organization research, so the 64-key increment describes that declared cohort. The useful output is the precise difference for these source files and a method that another analyst can reproduce on a different, declared cohort.

Method and reproducibility

Build a set P from nonempty location_key values within each organization CSV. Build a set S by splitting secondary_practice_addresses on semicolons, trimming values and removing empty values. Count P, S, S minus P and P union S. The increase percentage is |S minus P| divided by |P| times 100. Reuse the retained address-key strings without applying a second normalization algorithm. Compare only within a legal-name profile.

Limitations: These are address-key sets, not independently verified facilities. A secondary field can be incomplete or outdated. Zero additional keys means no additional values under this extraction and comparison rule; it does not prove a single-location business or complete coverage. String differences may survive normalization, and shared strings can conceal separate suites or departments. No key establishes current opening status, ownership or purchasing authority. Commercial value and operating-site classifications require separate evidence. The totals retain legal-name boundaries rather than globally deduplicating addresses.

This analysis and its prose were prepared with AI assistance. The 13 input CSV files are the downloads already attached to the organization profiles below. We read identifiers and ZIP codes as text, preserve legal names, and calculate each organization independently. The provenance file records every input SHA256 and row count. The analysis code is included with the reviewable source; the public CSV and JSON contain identical rows. The derived downloads contain aggregate organization-level counts and source-field summaries.

Underlying source: CMS NPPES dissemination files, identified in each profile as the September 2026 monthly release. The public CMS index was under maintenance when this analysis was prepared; we used the retained extracts and their recorded provenance, using the original recorded source vintage. CMS explains the NPI standard.

Source profiles and state drill-downs

• COSTCO WHOLESALE CORPORATION profile, source records and method
• CVS PHARMACY INC profile, source records and method
• GENOA HEALTHCARE LLC profile, source records and method
• LUXOTTICA OF AMERICA INC profile, source records and method
• NATIONAL VISION INC profile, source records and method
• NEW YORK CITY HEALTH AND HOSPITALS CORPORATION profile, source records and method
• NOVANT HEALTH MEDICAL GROUP, LLC profile, source records and method
• PUBLIX SUPER MARKETS INC profile, source records and method
• TOTAL RENAL CARE INC profile, source records and method
• WALGREEN CO profile, source records and method
• WALMART INC. profile, source records and method
• WAL-MART STORES EAST LP profile, source records and method
• WAL-MART STORES, INC profile, source records and method

Each profile links to its existing state page and filtered source records. Those pages provide the next level of detail; use them to inspect the records behind a legal-name distribution. Start with CVS Pharmacy Inc. in Texas, Novant Health Medical Group in North Carolina, or the organization research directory.